Geoscience Data Journal最新文献

WRFtailor is an open-source toolkit offering a set of distinct functionalities to customise and tailor the Weather Research and Forecasting (WRF) model input data, such as WPS geographical data or WRF/Chem emissions data. Before running the toolkit, the user should specify an area of interest (AOI) from the WRF input file, a variable to be tailored within the AOI, and a polynomial of variables that will replace the specified variable. WRFtailor is a Linux-based toolkit, written in Shell and NCAR Command Language (NCL) scripts, and is available at https://github.com/anikfal/wrftailor.

The volume of scientific data produced for and by numerical simulation workflows is increasing at an incredible rate. This raises concerns either in computability, interpretability, and sustainability. This is especially noticeable in earth science (geology, meteorology, oceanography, and astronomy), notably with climate studies. We highlight five main evaluation issues: efficiency, discrepancy, diversity, interpretability, availability. Among remedies, lossless and lossy compression techniques are becoming popular to better manage dataset volumes. Performance assessment—with comparative benchmarks—requires open datasets shared under FAIR principles (Findable, Accessible, Interoperable, Reusable), provided in a MWE (Minimal Working Example) with ancillary data for reuse. We share Lundi_sim, an exemplary faulted geological mesh. It is inspired by the SPE10 comparative Challenge. It is not meant to be compared to the latter for reservoir simulation. It is instead tailored—with power-of-two dimensions and additional faults—to both more challenging fluid displacement and upscaling methods, and allowing versatile compression benchmarks. Enhanced by porosity/permeability datasets, this dataset proposes four distinct subsurface environments. They were primarily designed for flow simulation in porous media. Several consistent resolutions (with HexaShrink multiscale representations) are proposed for each model. We also provide a set of reservoir features for reproducing typical two-phase flow simulations on all Lundi_sim models in a reservoir engineering context. This dataset is chiefly meant for benchmarking and evaluating data size reduction (upscaling) or genuine composite mesh compression algorithms. It is also suitable for other advanced mesh processing workflows in geology and reservoir engineering, from visualisation to machine learning. Lundi_sim meshes are available at 10.5281/zenodo.14641958.

Dataset obtained from a fully instrumented experimental urban street canyon, located on a research site in Montpellier (France, Mediterranean climate) and monitored between 21 July 2023 and 31 July 2024. This east–west canyon consists of two parallel concrete walls (height 2.3 m, length 12 m, width 5 m). Three nearly adjacent planters (height 0.9 m, length 2.3 m, width 0.8 m) are aligned along its inner north wall and contain climbing plants of the Lonicera japonica species. Three other nearly adjacent planters (height 0.9 m, length 2.3 m, width 1.1 m) are aligned along its inner south wall and contain shrub plants of the Abelia grandiflora species. Each planter includes 11 sensors to measure soil temperature, water content and matric potential, collecting data every 10 min. Drip irrigation was used and each series of planters received identical irrigation amounts. The irrigation strategy changed several times, to monitor the impacts of irrigation on the overall water balance of the soil–plant–atmosphere system, especially on the local microclimatic variables. A dense network of 102 sensors was installed to monitor wind direction and velocity, air temperature, relative humidity, radiation, grey globe temperature and rainfall at 1.3 m above the ground, which is a standardised measurement height for determining the variables used to quantify thermal comfort indices. This dataset supports (i) understanding thermal, radiative and aerodynamic processes in urban canyons, (ii) detecting irrigation impact on microclimate, (iii) validating CFD-based microclimate models and (iv) identifying methods to manage urban heatwaves through water resource optimisation. By encompassing a full year of seasonal and climatic variability, this study is the first to evaluate the combined effects of vegetation type and irrigation on urban thermal comfort in a Mediterranean context, providing a significant contribution to urban microclimate research.

Despite increasing availability of satellite-derived products, in situ glacier observations are pivotal to accurately monitor glacier change and to calibrate and validate glacier models. However, comprehensive multi-variable field observations are especially rare on large glaciers and on debris-covered glaciers. Here we present extensive field observations from Kennicott Glacier, a heavily debris-covered glacier in central Alaska covering more than 400 km². The multi-year data set includes point glacier mass balances, meteorological data from several weather stations on and off the glacier, debris thickness and temperature, ice cliff back wasting derived from time-lapse photography of horizontal stakes drilled into several cliffs, and bathymetry, water temperature, and water level of proglacial and supraglacial lakes. Cumulated summer melt of more than 8 m was observed at the lowest clean-ice sites. Melt rates over clean ice correlate well with elevation, while the rates over debris-covered ice lack any strong elevation dependence. Melt rates drop exponentially with increasing debris thickness and tend to be much lower than for clean ice at similar elevations. Melt rates determined for ice cliffs in areas of otherwise continuous debris cover were up to 10× those for debris-covered ice, and even exceeded standard clean ice melt rates. Debris-cover thickness measurements at 150 sites vary from < 1 to 69 cm with an average of 17 ± 11 cm (±standard deviation). Debris thickens down-glacier, but with high spatial variability–thickness was observed to vary by tens of cm within a ~15 m radius. Depth-averaged thermal heat conductivity derived from supraglacial debris temperature profiles at 12 sites ranges from 0.53 to 1.86 W m⁻¹ K⁻¹. Interconnected proglacial lakes covered 1.61 km² in 2018 with observed water depths of more than 60 m in the two largest lakes. The dataset can be downloaded at https://doi.org/10.5281/zenodo.14625691 (Petersen, Hock, Loso, Guo, et al., 2024) and will be useful for glaciological and glacier meteorological studies.

This paper introduces SCIBA, a novel dataset documenting floods (F) and landslides (L) triggered by rainfall that affected the road-railway network in the municipalities of Scilla and Bagnara (Calabria, Italy) between 1911 and 2024. The study addresses the central research question: How can historical rainfall-induced flood and landslide events be systematically documented and used to improve predictive models for early warning systems in transport infrastructure? In response, SCIBA offers a comprehensive, spatially and temporally detailed dataset aimed at supporting the Disaster Risk Reduction (DRR) community and researchers developing empirical models for forecasting rainfall thresholds that precede F and L events. The unique contribution of this work lies in the systematic compilation and georeferencing of 281 historical FL events—a rare and valuable resource in a context where such data are typically fragmented or unavailable. SCIBA bridges this gap through extensive archival research, such as the State Archive, the Regional Civil Protection archive, and ANAS, the agency responsible for state roads in the region. All the records include the spatial references (geographic coordinates and place names) and temporal localization (to the day, and in 18.6% of cases, the exact hour). Moreover, each record integrates daily rainfall data from two operational rain gauges (Scilla at 73 m a.s.l. and Bagnara at 30 m a.s.l.) for the day of the event and the preceding 4 days, enabling analysis of both daily and cumulative rainfall as triggering factors. Despite some unavoidable gaps in historical documentation, SCIBA stands out as a ready-to-use dataset that supports the development of cause-effect models for rainfall-induced hazards. Provided in GIS format, the dataset not only enhances understanding of past events but also identifies critical hotspots for monitoring during intense rainfall, contributing directly to emergency planning, traffic management, and the resilience of transport networks.

This manuscript documents the data rescue, digitization, and quality assurance of archival daily maximum and minimum water levels at twenty-five sites within the Sacramento-San Joaquin Delta. The records encompass 1846 total unique years, where 915 years are newly digitized from the 1915–1985 era. The period of record for each gauge location varies from 40 to 109 years (median = 80 years). Quality assurance procedures and datum corrections were applied to both archival and digital records to generate a time series referenced to a common geocentric datum. Both riverine and coastal influences on mean sea level and great diurnal range are evident in the time series. During the winter months, when river discharge is large, mean sea-level increase and great diurnal ranges decrease. The strongest river influence is observed at more landward sites, where daily mean sea levels can increase by 1–10 m. The data also include spatially and interannually varying extreme water levels and show evidence of the influence of seasonal tidal barrier construction/dismantling, which began in the late 1980s. The data records thus enable future analysis of multiple intertwined issues, including sea-level rise, subsidence, tides, climate patterns, atmospheric conditions, shoreline/habitat changes, bathymetric modifications, water resource management, and flood hazards.

The ongoing and post-war reconstruction of Ukrainian water resources is critical for food production, public health, energy, industry and environmental protection. This effort, the most ambitious in Europe since World War II, faces challenges due to a lack of accessible decision-support tools for managing water ecosystems effectively. In response, we developed a high-resolution hydrological model of the Ukrainian Watershed using the SWAT (Soil and Water Assessment Tool) model to assess water balance across all nine major river basins, covering an area of 873,600 km². The model is integrated into an interactive web interface—named ‘Land & Water’—which provides public access to model inputs and outputs and was designed considering FAIR (Findable, Accessible, Interoperable, Reusable) principles. A multifaceted calibration approach, combining soft and hard methods, ensures balanced performance for surface, lateral and groundwater dynamics. The platform enables users to visualise and download model results, supporting both experts and non-experts in water-related decision making. The study demonstrates how the model helps close critical data gaps—providing estimates of river discharge for transboundary inflows, total and groundwater flow around the Kakhovka reservoir, and potential transpiration and crop growth to assess irrigation needs. Overall, the dataset offers a valuable tool for Ukraine's recovery, fosters transparent water governance, and supports environmental research on water quality, climate adaptation and sustainable agriculture.

Landslide inventories support both post-event response and predictive model evaluation, but it remains challenging to create public, current, comprehensive and accurate landslide inventories. In response to this need, thousands of rainfall-triggered landslides were mapped and organised within the National Aeronautics and Space Administration's Cooperative Open-Online Landslide Repository (COOLR), which contains over 11,000 landslide reports from the Global Landslide Catalogue. Recently, 22 inventories containing thousands of rainfall-triggered landslides have been added to COOLR, which was reorganised to better accommodate large landslide inventories. All the data are available on the ‘Landslide Viewer’ web application, which also shows referenced and imported landslide inventories from other researchers. The new inventories are each connected to a landslide-triggering rainfall event, and therefore their date of occurrence was usually known. Landslide events were found by searching through credible sources or due to an external request for support during a disaster response. In either case, high-resolution imagery was utilised to digitise the landslides in the region. The resulting data can be used for various purposes, such as model training and validation. To demonstrate their potential, satellite precipitation was analysed with reference to the new inventories. The precipitation analysis highlights the potential of daily satellite precipitation estimates in areas with limited ground precipitation observations. Some of the heavy precipitation events were underestimated, but many were captured and could inform future landslide hazard assessment.

Climate change has significant impacts on hydrological fluxes worldwide, with pronounced effects in Brazil, including intense and recurrent droughts and floods. Accurate streamflow prediction is therefore essential for advancing water resources engineering, improving water resources management, and informing climate adaptation strategies. Here, we introduce the Streamflow Scenarios Projections for Brazilian Catchments (SSP-CABra), which provides long-term to daily streamflow simulations for 735 Brazilian catchments. These simulations are generated using five hydrological models of varying complexity, forced by 10 bias-corrected CMIP6-based climate simulations for historical (1980–2013) and future (2015–2100; SSP2-4.5 and SSP5-8.5) periods. SSP-CABra addresses a critical gap in large-scale hydrological modelling for Brazil, offering valuable insights for researchers and policymakers. Despite its broad applicability, the dataset includes models with varying performance across regions; users should assess model skill locally to ensure appropriate use, particularly for decision-making or extreme event analyses. Still, by leveraging multiple models and climate scenarios, SSP-CABra supports not only the mitigation of climate change impacts on water security, but also advances the understanding of model performance and regional hydrological behaviour.

Landslides are among the most widespread and recurrent natural hazards, posing significant threats due to their sudden onset and unpredictable nature. Understanding their spatial distribution patterns is crucial for improving disaster prediction and mitigation strategies. This study investigates the spatial distribution of 8349 landslide relics in the northern half of the Taihang Mountains, China, by analysing their relationship with eight influencing factors: elevation, slope, aspect, curvature, distance from rivers, distance from faults, lithology and land cover. The analysis is based on four key metrics: Landslide Number, Landslide Area, Landslide Number Density (LND) and Landslide Area Percentage (LAP). Results reveal that 66.06% of landslides (5515 events) and 40.79% of the total landslide area (61.84 km²) occur within the elevation range of 800–1300 m. The highest LND (1.04 km⁻²) and LAP (2.65%) appear in areas with curvature less than −10, indicating a preference for concave terrain. Slopes of 15°–20° host the most landslides (1713 events), whereas areas with slopes > 45° show the densest distribution (LAP = 1.44%). North- and northwest-facing slopes exhibit the highest susceptibility, with LNDs of 0.51 and 0.43 km⁻², respectively. Landslides are concentrated within 0–4 km of rivers (81.35% of total number), and proximity to faults strongly influences landslide size and density. The Quaternary loess and Neoproterozoic clay strata are particularly prone to landslides, with the Neoproterozoic displaying the highest LND (5.59 km⁻²). In terms of land cover, grasslands account for 48.4% of landslide occurrences, whereas forests contain over half of the total landslide area. Barren lands, though limited in extent, show the highest LND (0.95 km⁻²) and LAP (1.6%). These findings contribute to a deeper understanding of landslide susceptibility in the region and provide a scientific basis for future landslide risk assessments. Additionally, the results offer valuable insights for local governments in formulating targeted landslide prevention and mitigation strategies.